Field of the Invention
The present invention relates to a technology for position and orientation measurement of a measurement target object.
Description of the Related Art
Automation by using a robot is being progressed to improve a production efficiency in a production site. In a construction operation by the robot, a target object is to be recognized to obtain an accurate position and orientation. As a method of realizing this, a method of using a luminance image or a range image in which the target object is picked up has been developed. Since noise generated by various causes coexists in the luminance image or the range image, a method having high robustness with respect to the noise is preferably employed.
For example, according to Japanese Patent Laid-Open No. 2012-26974, position and orientation measurement of a camera is performed by a model fitting technique with which measurement data obtained from the luminance image or the range image in which the target object is picked up is fitted to a geometric model. Specifically, an approximate position and orientation of the target object are prepared, and the position and orientation are optimized such that an edge extracted from the luminance image or a distance value obtained from the range image is more appropriately fitted to the geometric model. As a technique to increase the robustness in the position and orientation measurement of the camera, a method of generating a plurality of approximate positions and orientations at predetermined sampling intervals in ranges that may be taken by the position and orientation of the camera, performing iterative calculation of the fitting in the respective approximate position and orientation, and adopting a highest-ranting fitting result as a fitting final result has been proposed.
However, according to the method described in Japanese Patent Laid-Open No. 2012-26974, in a case where noise such as shadow or specular reflection exists in the luminance image or the range image in which the target object is picked up, the optimal position and orientation may not be obtained in some cases, and the accuracy and the robustness are reduced. For example, a local solution may occur while the edge due to the shadow existing around the target object is erroneously associated with the edge of the geometric model, and a correct fitting result may not be obtained in some cases.